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To assist you in your R adventures in Alteryx, we've developed a R Tool Cheat Sheet which you can download to have as your very own. This article reviews and explains the functions included in the Alteryx - R cheat sheet.
R is an open-source programming language and software environment, specifically intended for statistical computing and graphics. The Alteryx Predictive Tools install includes an installation of R, along with a set of R Packages used by the Predictive Tools. This article describes how to determine which R packages (and versions) are installed for used with your Alteryx R Tool, as well as a few Alteryx-specific packages on Github.
Neural Networks are frequently referred to as "black box" predictive models. This is because the actual inner workings of why a Neural Network sorts data the way it does are not explicitly available for interpretation. A wide variety of work has been conducted to make Neural Networks more transparent, ranging from visualization methods to developing a Neural Network model that can “show it’s work”. This article demonstrates how to leverage the NeuralNetTools R package to create a plot of the Neural Network trained by the Alteryx Neural Net tool.
With the introduction of the Predictive Analytics Starter Kit, you can enhance your analytic skills through an interactive, guided starter kit that teaches core predictive modeling techniques (A/B testing, linear regression, and logistic regression)
Time series forecasting is using a model to predict future values based on previously observed values. In a time series forecast, the prediction is based on history and we are assuming the future will resemble the past. We project current trends using existing data.
Predictive Grouping is an approach that allows users to assess and create the appropriate number of clusters (groups) for their data to be assigned based on their similarity to each other in the same cluster and dissimilar to other data assigned to other clusters. K-Centroids represent a class of algorithms for doing what is known as partitioning cluster analysis. These methods work by taking the records in a database and dividing (partitioning) them into the best K groups based on some criteria. The purpose of creating clusters is to assist you in the business decision-making process as it relates to the clustered data.
Alteryx Designer comes with tools (based on both R and Python) to create and use predictive models without needing to write any code. But what if you've got custom models written in R or Python outside of Designer that you want to use in Designer, or vice versa?